Multi-phase Mandible-Anchored Automated Segmentation of Oropharyngeal GTVs in FDG-PET/CT

Accurate delineation of gross tumor volumes (GTVs) in oropharyngeal carcinoma remains central to radiotherapy (RT) planning, and recent advances in automated segmentation are beginning to influence multi-ple aspects of clinical workflow. However, automated segmentation of head and neck primary tumors and nodal metastases in multi-modal FDG-PET/CT is challenging due to anatomical complexity and varying image resolution. We describe the DLaBella29 team’s approach for Task 1 of the MICCAI 2025 HECKTOR challenge, which involves a multi-phase deep learning pipeline for GTVp and GTVn segmentation. Our method leverages the MONAI Auto3DSeg framework with a 3D SegResNet backbone, trained on co-registered PET/CT scans using a cross-validation strategy (3/7/15 folds across Phases). Key innovations include a mandible-anchored region-of-interest cropping strategy derived from automated mandible segmentation to focus the model on the oropharyngeal region and improve efficiency. A multi-phase seg-mentation pipeline was employed: an initial Phase infers GTVp and GTVn in the focused anatomical region-of-interest, and a second Phase independently refines these predictions. Post-processing ensues to merge GTVp and GTVn outputs to the original image coordinates. On the HECKTOR 2025 test set, our more limited and resource-constrained algorithm achieved a primary tumor Dice Similarity Coefficient (DSC) of 0.5289, an aggregated nodal DSC of 0.6156, and a GTVn detection F1-score of 0.5561, indicating fair performance in the challenge. This paper details the clinical context, methodology, cross-validation and testing results, and the implications of PET/CT-guided automated segmentation for oropharyngeal carcinoma.

Duke Scholars

Author Jared Rex Robbins Radiation Oncology

Author Dominic Angelo LaBella Radiation Oncology